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Task056_VerSe2019.py
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Task056_VerSe2019.py
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import subprocess
from collections import OrderedDict
import SimpleITK as sitk
from multiprocessing.pool import Pool
from nnUNet.nnunet.configuration import default_num_threads
from nnUNet.nnunet.dataset_conversion.Task056_Verse_normalize_orientation import normalize_slice_orientation, read_image, \
save_image, restore_original_slice_orientation
from nnUNet.nnunet.paths import nnUNet_raw_data
from batchgenerators.utilities.file_and_folder_operations import *
import shutil
from medpy import metric
import numpy as np
import os
def load_corr_save(in_folder: str, out_folder: str, filename: str):
assert filename.endswith(".nii.gz")
maybe_mkdir_p(out_folder)
img, header = read_image(join(in_folder, filename))
img_corr, header_corr = normalize_slice_orientation(img, header)
# now we save without restoring original slice orientation. We pickle the header for later
save_image(img_corr, header_corr, join(out_folder, filename))
save_pickle(header, join(out_folder, filename[:-7] + ".pkl"))
# just a test to see if we can reproduce the original image
# img_corr2, header_corr2 = restore_original_slice_orientation(img_corr, header_corr)
# save_image(img_corr2, header_corr2, join(out_folder, filename[:-7] + "_re.nii.gz"))
# seems to work
def evaluate_verse_case(sitk_file_ref:str, sitk_file_test:str):
"""
Only vertebra that are present in the reference will be evaluated
:param sitk_file_ref:
:param sitk_file_test:
:return:
"""
gt_npy = sitk.GetArrayFromImage(sitk.ReadImage(sitk_file_ref))
pred_npy = sitk.GetArrayFromImage(sitk.ReadImage(sitk_file_test))
dice_scores = []
for label in range(1, 26):
mask_gt = gt_npy == label
if np.sum(mask_gt) > 0:
mask_pred = pred_npy == label
dc = metric.dc(mask_pred, mask_gt)
else:
dc = np.nan
dice_scores.append(dc)
return dice_scores
def evaluate_verse_folder(folder_pred, folder_gt, out_json="/home/uesrname/nnUnet/verse.json"):
p = Pool(default_num_threads)
files_gt_bare = subfiles(folder_gt, join=False)
assert all([isfile(join(folder_pred, i)) for i in files_gt_bare]), "some files are missing in the predicted folder"
files_pred = [join(folder_pred, i) for i in files_gt_bare]
files_gt = [join(folder_gt, i) for i in files_gt_bare]
results = p.starmap_async(evaluate_verse_case, zip(files_gt, files_pred))
results = results.get()
dct = {i: j for i, j in zip(files_gt_bare, results)}
results_stacked = np.vstack(results)
results_mean = np.nanmean(results_stacked, 0)
overall_mean = np.nanmean(results_mean)
save_json((dct, list(results_mean), overall_mean), out_json)
if __name__ == "__main__":
base = "/home/uesrname/nnUnet/dataset/"
base_corrOrient = "/home/uesrname/nnUnet/dataset_corrOrient/"
if not os.path.exists(base_corrOrient):
os.makedirs(base_corrOrient)
# correct orientation
train_files_base = subfiles(join(base, "train"), join=False, suffix="_seg.nii.gz")
train_segs = [i[:-len("_seg.nii.gz")] + "_seg.nii.gz" for i in train_files_base]
train_data = [i[:-len("_seg.nii.gz")] + ".nii.gz" for i in train_files_base]
test_files_base = [i[:-len(".nii.gz")] for i in subfiles(join(base, "test"), join=False, suffix=".nii.gz")]
test_data = [i + ".nii.gz" for i in test_files_base]
for i in train_segs + train_data:
load_corr_save(join(base, "train"), join(base_corrOrient, "train"), i)
for i in test_data:
load_corr_save(join(base, "test"), join(base_corrOrient, "test"), i)
train_files_base = subfiles(join(base_corrOrient, "train"), join=True, suffix="_seg.nii.gz")
train_segs = [i[:-len("_seg.nii.gz")] + "_seg.nii.gz" for i in train_files_base]
train_data = [i[:-len("_seg.nii.gz")] + ".nii.gz" for i in train_files_base]
test_files_base = [i[:-len(".nii.gz")] for i in subfiles(join(base_corrOrient, "test"), join=True, suffix=".nii.gz")]
test_data = [i + ".nii.gz" for i in test_files_base]
task_id = 56
task_name = "VerSe"
foldername = "Task%03.0d_%s" % (task_id, task_name)
out_base = join(nnUNet_raw_data, foldername)
imagestr = join(out_base, "imagesTr")
imagests = join(out_base, "imagesTs")
labelstr = join(out_base, "labelsTr")
maybe_mkdir_p(imagestr)
maybe_mkdir_p(imagests)
maybe_mkdir_p(labelstr)
train_patient_names = [i[:-len("_seg.nii.gz")] for i in subfiles(join(base_corrOrient, "train"), join=False, suffix="_seg.nii.gz")]
for p in train_patient_names:
curr = join(base_corrOrient, "train")
label_file = join(curr, p + "_seg.nii.gz")
image_file = join(curr, p + ".nii.gz")
shutil.copy(image_file, join(imagestr, p + "_0000.nii.gz"))
shutil.copy(label_file, join(labelstr, p + ".nii.gz"))
test_patient_names = [i[:-7] for i in subfiles(join(base_corrOrient, "test"), join=False, suffix=".nii.gz")]
for p in test_patient_names:
curr = join(base_corrOrient, "test")
image_file = join(curr, p + ".nii.gz")
shutil.copy(image_file, join(imagests, p + "_0000.nii.gz"))
json_dict = OrderedDict()
json_dict['name'] = "VerSe2019"
json_dict['description'] = "VerSe2019"
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = "see challenge website"
json_dict['licence'] = "see challenge website"
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "CT",
}
json_dict['labels'] = {i: str(i) for i in range(26)}
json_dict['numTraining'] = len(train_patient_names)
json_dict['numTest'] = len(test_patient_names)
json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i.split("/")[-1], "label": "./labelsTr/%s.nii.gz" % i.split("/")[-1]} for i in
train_patient_names]
json_dict['test'] = ["./imagesTs/%s.nii.gz" % i.split("/")[-1] for i in test_patient_names]
save_json(json_dict, os.path.join(out_base, "dataset.json"))
# run this part of the code once training is done
folder_gt = "/home/username/nnUnet/nnUNet_raw/nnUNet_raw_data/Task056_VerSe/labelsTr"
folder_pred = "/home/username/nnUnet/results/3d_fullres/Task056_VerSe/nnUNetTrainerV2__nnUNetPlansv2.1/cv_niftis_raw"
out_json = "/home/username/nnUnet/results/3d_fullres/Task056_VerSe_3d_fullres_summary.json"
evaluate_verse_folder(folder_pred, folder_gt, out_json)
folder_pred = "/home/username/nnUnet/results/3d_lowres/Task056_VerSe/nnUNetTrainerV2__nnUNetPlansv2.1/cv_niftis_raw"
out_json = "/home/username/nnUnet/results/3d_lowres/Task056_VerSe_3d_lowres_summary.json"
evaluate_verse_folder(folder_pred, folder_gt, out_json)
folder_pred = "/home/username/nnUnet/results/3d_cascade_fullres/Task056_VerSe/nnUNetTrainerV2CascadeFullRes__nnUNetPlansv2.1/cv_niftis_raw"
out_json = "/home/username/nnUnet/results/3d_cascade_fullres/Task056_VerSe_3d_cascade_fullres_summary.json"
evaluate_verse_folder(folder_pred, folder_gt, out_json)